Improved Grey Wolf Optimizer and Their Applications
Liang Xu, Di Wang, Ming Huang · 2019
Grey Wolf Optimizer is a kind of artificial intelligence optimization algorithm. Aiming at the problem of local optimum and low convergence accuracy of GWO, this paper uses reverse learning strategy to generate initial population to increase population diversity; and uses simulated annealing algorithm's strong ability to jump out of local optimum solution to make up for the shortcoming that GWO is easy to fall into local optimum; finally, the first three individuals of population fitness are mutated to improve the improvement of the algorithm. The speed and accuracy of the algorithm are improved to avoid falling into local optimum. The superiority of the improved algorithm is verified by simulation experiments.